As can be observed in the table:
(1) our proposed models trained on different ratios of labeled target data outperform all existing methods within all three categories of UDA, SSL and SSDA;
(2) With 100 labeled target examples the proposed approach outperforms all state-of-the-art UDA methods thus demonstrating the importance of semi-supervision.
(3) With 200 labeled target examples the proposed approach significantly narrows the performance gap with a fully supervised DeepLabV2 approach, thus demonstrating that the proposed approach is able to effectively utilize information from unlabeled target examples.
(4) With any amount of labeled target examples the proposed approach outperforms all SSL approaches thus demonstrating it can effectively utilize data from a different domain.
(5) The proposed approach outperforms at all mixing levels the state-of-the-art SSDA approach of Chen et al. [1]} (DLDM) which does not take into account intra-domain adaptation within the target domain, thus demonstrating the advantage of the dual domain adaptation in the proposed approach.
(6) The proposed method "Ours\(^{2}\) " using ComplexMix performs better than the "Ours\(^{1}\) " method using ClassMix for lower labeled data ratios thus demonstrating the importance of more complex mixing when the amounts of labeled target data are small.
